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            大数据，统计精度与计算复杂度
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           12月
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           18
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           2012
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          <p>
           大数据好热好热啊！统计的时代好像飘然而至了。可我们准备好了吗？答案吗，当然还是，“it depends”。
          </p>
          <p>
           最近看到的很多在大数据上有进展的问题基本上是预测问题，比如：搜索问题，语音图像识别问题，推荐系统问题，广告放送问题，社交网络问题。。。 问题的答案和算法的结果一般是一个或一串预测值。算法也是百花齐放：各类回归，各类聚类，矩阵分解，深度学习。。。
          </p>
          <p>
           这些预测值的准确性基本是靠算法在独立验证数据集上的表现来刻画。忽然间统计的一些基本概念，像标准差，置信区间，一类二类错误，等等都被大数据淹没的无影无踪。当忽然间体会到理想很丰满，现实很骨感的书本与实践的差距时，摆弄惯了$1 / \sqrt{n}$ 的统计人（我也算一员）有些无所适从。
          </p>
          <p>
           在数据和算法当道的时代，有时不仅要问“大数据和统计还有关系吗？” 不妨听听这个报告：
          </p>
          <blockquote>
           <p>
            <em>
             加州大学伯克利分校电气工程与计算机系、统计学系教授Michael Jordan，在
             <a href="http://huati.weibo.com/k/21%E4%B8%96%E7%BA%AA%E7%9A%84%E8%AE%A1%E7%AE%97%E5%A4%A7%E4%BC%9A?from=510">
              #21世纪的计算大会#
             </a>
             上做”大数据的分治和统计推断”的主题演讲，就解决大数据集中获得置信区间和大型矩阵求全法的问题，提出了新的解决方案。
            </em>
           </p>
          </blockquote>
          <p>
           <embed align="middle" allowfullscreen="true" allowscriptaccess="always" height="400" quality="high" src="http://player.youku.com/player.php/sid/XNDg3ODE1MzU2/v.swf" type="application/x-shockwave-flash" width="480">
           </embed>
          </p>
          <p>
           讲座主要是讲大数据下统计和计算的结合问题，同时提出用subsample的方法在大数据上来建立bootstrape 置信区间的。对，不光是关心估计或预测，同时想给出置信区间。统计方法对不确定性的刻划在大数据一样重要，只不过大家还没有足够精力和计算能力来估计它。
          </p>
          <p>
           如果你没太听懂这讲座中提到的方法，没关系。可以跳到 33分钟听结尾的一席话。
          </p>
          <blockquote>
           <p>
            If you didn’t understand a lot of the talk, it is a bit technical, maybe you should go take some statistics class if you are a computer scientist. If you are a statistician, I don’t know if any of you are here, you probably should take some computer science classes.
           </p>
           <p>
            The future is for those people who can take these two fields and integrate them into one brain, not having to bring every project to statisticians, trying to have them talking to computer scientists using some translation software. But one brain can bring these ideas jointly together. How do I think about my statistical risk and the errors I’m going to make when data come into a computer and how do I think about stating that so I don’t have to break when I have more than a few hundred thousand data points.
           </p>
           <p>
            That’s just not what we have been teaching students. And, so, we are still not doing a good job in teaching students, and we may talk about that later. Even though there are classes on this, you guys can go and learn it by yourself. If you are taking computer science classes, take a few stats classes. Pick some books, sit them by your bed and read them every night, and make sure you can do these two things together. This is the future of the field.
           </p>
           <p>
            Thank you!
           </p>
          </blockquote>
          <p>
           既然报告中提到了翻译软件，我也就随机的试了一下百度翻译：
          </p>
          <blockquote>
           <p>
            如果你不懂的许多谈话，这是一个有点技术，也许你应该采取一些统计类如果你是电脑科学家。如果你是一个统计学家，我不知道如果你在这里，你也许应该采取一些计算机科学课。
           </p>
           <p>
            未来是为这些人谁可以把这两个领域和整合成一个大脑，不必把每一个项目的统计人员，想让他们谈论计算机科学家使用翻译软件。但一个大脑可以把这些想法联合在一起。我怎么觉得我的统计风险和错误，我要当数据进入计算机，我怎么想说，所以我没有休息的时候，我已经超过几十万的数据点。
           </p>
           <p>
            这不是我们所教学生。和，那么，我们还是做得不好的学生在教学中，我们可以谈一下。即使有课上，你们可以去学习一下。如果你是以计算机科学课，带几个属性类。拿一些书，坐在你的床上，他们每天晚上读他们，并确保你能做到这两件事一起。这是该领域的未来。
           </p>
           <p>
            谢谢！
           </p>
          </blockquote>
          <p>
           还不错。最后多说一句，第一次见Michael Jordan穿的如此正式 🙂
          </p>
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            施涛
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          at 04:18
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          Tagged with:
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          ,
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           大数据，Michael Jordan
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       <article class="full-content post-2101 post type-post status-publish format-standard hentry category-63 category-61 category-84 category-3 tag-andrew-gelman tag-bayesian tag-frequentist tag-larry-wasserman tag-nate-silver tag-the-signal-and-the-noise category-63-id category-61-id category-84-id category-3-id post-seq-2 post-parity-even meta-position-corners fix" id="post-2101">
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2012/12/07/frequentist-or-bayesian/" rel="bookmark" title="大话统计：Frequentist or Bayesian">
            大话统计：Frequentist or Bayesian
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             数据分析
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             统计
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             闲扯
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          <span class="month">
           12月
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          <span class="day">
           07
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          <span class="year">
           2012
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          <p>
           [前言：先向无法科学上网的客官作揖抱歉了，有些江湖野史可能被墙]
          </p>
          <p>
           话说江湖风云人物，神算子
           <a href="http://en.wikipedia.org/wiki/Nate_Silver" target="_blank">
            Nate Silver
           </a>
           ， 在纽约时报
           <a href="http://fivethirtyeight.blogs.nytimes.com" target="_blank">
            开博占卜
           </a>
           各项江湖话题，每每言中。并著书立传，
           <a href="http://www.us.penguingroup.com/static/pages/features/the_signal_and_the_noise.html" title="The Signal and the Noise - Books by Nate Silver - Penguin Group (USA)">
            The Signal and the Noise
           </a>
           ，一时引来
           <a href="http://cos.name/2012/11/the-rise-of-data-scientists/" target="_blank">
            各相追捧
           </a>
           。
          </p>
          <p>
           这日，曾留下武林秘籍
           <a href="http://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/0387402721/" target="_blank">
            All of Statistics: A Concise Course in Statistical Inference
           </a>
           的大侠
           <a href="http://www.stat.cmu.edu/~larry/" target="_blank">
            Larry Wasserman
           </a>
           (Department of Statistics, Department of Machine Learning, Carnegie Mellon University) 突发感想，在自己博客
           <a href="http://normaldeviate.wordpress.com/" target="_blank">
            Normal Deviate
           </a>
           中写下大号书评：
          </p>
          <h2>
           <a href="http://taoshistat.wordpress.com/2012/12/06/2100/" rel="bookmark" title="Permanent Link to Nate Silver is a Frequentist: Review of ``the signal and the noise''">
            Nate Silver is a Frequentist: Review of “the signal and the noise”
           </a>
          </h2>
          <blockquote>
           <p>
            ［……］As you can see, I liked the book very much and I highly recommend it.
           </p>
           <p>
            But …
           </p>
           <p>
            I have one complaint. Silver is a big fan of Bayesian inference, which is fine. Unfortunately, he falls into that category I referred to
            <a href="http://normaldeviate.wordpress.com/2012/11/17/what-is-bayesianfrequentist-inference/">
             a few posts ago.
            </a>
            He confuses “Bayesian inference” with “using Bayes’ theorem.” His description of frequentist inference is terrible. He seems to equate frequentist inference with Fisherian significance testing, most using Normal distributions. Either he learned statistics from a bad book or he hangs out with statisticians with a significant anti-frequentist bias.
           </p>
           <p>
            Have no doubt about it: Nate Silver is a frequentist. For example, he says:
           </p>
           <p>
            <strong>
             “One of the most important tests of a forecast — I would argue that it is the single most important one — is called calibration. Out of all the times you said there was a 40 percent chance of rain, how often did rain actually occur? If over the long run, it really did rain about 40 percent of the time, that means your forecasts were well calibrated.”
            </strong>
           </p>
           <p>
            It does not get much more frequentist than that. And if using Bayes’ theorem helps you achieve long run frequency calibration, great. If it didn’t, I have no doubt he would have used something else. But his goal is clearly to have
            <strong>
             good long run frequency behavior
            </strong>
            .［……］
           </p>
          </blockquote>
          <p>
           一时间风雨突起，这个关于 Frequentist or Bayesian 的争议话题又现江湖。第一个杀出来的是？没错！正是大侠
           <a href="http://andrewgelman.com/" rel="external nofollow">
            Andrew Gelman
           </a>
           (Department of Statistics, Columbia University)，著名的
           <a href="http://andrewgelman.com/" title="Statistical Modeling, Causal Inference, and Social Science: ">
            Statistical Modeling, Causal Inference, and Social Science
           </a>
           的博主。
          </p>
          <blockquote>
           <p>
            Larry:
           </p>
           <p>
            There is such a thing as Bayesian calibration of probability forecasts. If you are predicting a binary outcome y.new using a Bayesian prediction p.hat (where p.hat is the posterior expectation E(y.new|y), then Bayesian calibration requires that E(y.new|y.hat) = y.hat for any y.hat. This isn’t the whole story (as always, calibration matters but so does precision), but it’s not the same as frequentist calibration or unbiasedness. In frequentist calibration, the expectation is taken conditional on the value of the unknown parameters theta in the model. The calibration you describe above (for another example, see
            <a href="http://andrewgelman.com/2010/11/some_thoughts_o_8/" rel="nofollow">
             here
            </a>
            and scroll down) is unconditional on theta, thus Bayesian. So I disagree with you that those calibrations are frequentist and not Bayesian. But of course I completely agree with you that the concept of frequency performance of methods is important. It’s just that Bayesian calibration does not condition on theta.
           </p>
          </blockquote>
          <p>
           江湖不宁，争论又起，各路人马
           <a href="http://normaldeviate.wordpress.com/2012/12/04/nate-silver-is-a-frequentist-review-of-the-signal-and-the-noise/#comments" target="_blank">
            加入论战
           </a>
           。大侠
           <a href="http://xianblog.wordpress.com" target="_blank">
            XI’AN’ OG
           </a>
           也出面提出把这场论战记入江湖册：
          </p>
          <blockquote>
           <p>
            Would you mind publishing this review in
            <a href="http://chance.amstat.org" target="_blank">
             CHANCE
            </a>
            , by any chance???
           </p>
          </blockquote>
          <p>
           看大侠们挥舞这各项理论和哲学思想，小的不尽倒吸一口凉气。
          </p>
          <p>
           问题原来是神算子是否属于少林派，还是武当派。怎么忽然就变成各派根据其所著之书，来统计推断其个人取向了呢？然后有演变为了两派镇派宝典根基的争论了。
          </p>
          <p>
           要是实在关心神算子心仪那派的问题，不如找来神算子本人问一下，到底人家对哪里更有归属感，不就行了。实在找不到本人，就@他，tweet 一下好了。。。。。。
          </p>
          <p>
           走你！
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           <a class="entry-title" href="http://blog.cos.name/taoshi/2012/12/05/blue-marble-black-marble-our-planet/" rel="bookmark" title="白天不懂夜的黑">
            白天不懂夜的黑
           </a>
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         <div class="date">
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           12月
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           05
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           2012
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          <p>
           大数据时代来了，但什么样的问题才需要大数据呢？我觉得答案挺直接的：大问题，没法简单抽样或抽样也没法解决的问题。
          </p>
          <p>
           今天很火爆的一张图是NASA用2.5兆兆字节(terabytes)卫星数据画的，地球的全球夜间灯光亮度图。
          </p>
          <div class="wp-caption aligncenter" style="width: 710px">
           <a href="http://eoimages.gsfc.nasa.gov/images/imagerecords/79000/79765/dnb_land_ocean_ice.2012.3600x1800.jpg">
            <img src="http://i.imgur.com/f6thJ.jpg"/>
           </a>
           <p class="wp-caption-text">
            Credit: Visible Infrared Imaging Radiometer Suite (NASA)
           </p>
          </div>
          <p>
           数据是NASA的
           <a href="http://npp.gsfc.nasa.gov/viirs.html" rel="bookmark" target="_blank">
            Visible Infrared Imaging Radiometer Suite
           </a>
           在今年4月和10月收集的，然后要经过一系列处理，去除极光，森林着火的火光，月光反射，等等各种光线，剩下的是估计的城市光线。
          </p>
          <blockquote>
           <p>
            The night time view of Earth was made possible by the “day-night band” of the
            <a href="http://npp.gsfc.nasa.gov/viirs.html" rel="bookmark" target="_blank">
             Visible Infrared Imaging Radiometer Suite
            </a>
            . VIIRS detects light in a range of wavelengths from green to near-infrared and uses filtering techniques to observe dim signals such as gas flares, auroras, wildfires, city lights, and reflected moonlight. In this case, auroras, fires, and other stray light have been removed to emphasize the city lights.
           </p>
          </blockquote>
          <p>
           这个我觉得是大数据的一个强项。要画这么精确，高分辨率的图，没有所有的数据和认真的分析是没法想象的。NASA提供的最高分辨率的图在这里(
           <a href="http://eoimages.gsfc.nasa.gov/images/imagerecords/79000/79765/dnb_land_ocean_ice.2012.54000x27000_geo.tif" target="_blank">
            384 MB, TIFF, 54000×27000
           </a>
           )，没有足够内存建议别碰。不过NASA搞了个灰常了得的幻灯片
           <a href="http://www.flickr.com//photos/gsfc/sets/72157632175125121/show/" target="_blank">
            Black Marble- City Lights 2012
           </a>
           。值得一看。
          </p>
          <p>
           以前这夜间图可是只有军方卫星才能看到的，现在也可以娱乐大众了。
          </p>
          <blockquote>
           <p>
            A handful of scientists have observed earthly night lights over the past four decades with military satellites and astronaut photography. But in 2012, the view became significantly clearer. The
            <a href="http://npp.gsfc.nasa.gov/" target="_blank">
             Suomi National Polar-orbiting Partnership
            </a>
            (NPP) satellite — launched in October 2011 by NASA, the National Oceanic and Atmospheric Administration (NOAA), and the Department of Defense — carries a low-light sensor that can distinguish night lights with six times better spatial resolution and 250 times better resolution of lighting levels (dynamic range) than before. Also, because Suomi NPP is a civilian science satellite, data is available to scientists within minutes to hours of acquisition.
           </p>
          </blockquote>
          <p>
           白天的卫星数据我接触多了，但晚上的一直是望而却步。大数据来了，从原始数据到最后的图，每一步都有统计方法在支持。到最后，白天和黑夜的分别也不是那么明显了，那些方法还叫不叫统计也不重要了。
          </p>
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